Prognostic prediction of dengue hemorrhagic fever in pediatric patients with suspected dengue infection: A multi-site study

登革热 医学 登革热出血热 急诊医学 登革出血热 概化理论 登革热病毒 前瞻性队列研究 重症监护室 重症监护医学 儿科 内科学 免疫学 数学 统计
作者
Myat Su Yin,Peter Haddawy,Panhavath Meth,Araya Srikaew,Chonnikarn Wavemanee,Saranath Lawpoolsri Niyom,Kanokwan Sriraksa,Wannee Limpitikul,Preedawadee Kittirat,Nasikarn Angkasekwinai,Oranich Navanukroh,Arunee Mapralub,Supansa Pakdee,Chotika Kaewpuak,Nattaya Tangthawornchaikul,Prida Malasit,Panisadee Avirutnan,Dumrong Mairiang
出处
期刊:PLOS ONE [Public Library of Science]
卷期号:20 (8): e0327360-e0327360
标识
DOI:10.1371/journal.pone.0327360
摘要

Dengue virus (DENV) infection is a major global health problem. While DENV infection rarely results in serious complications, the more severe illness dengue hemorrhagic fever (DHF) has a significant mortality rate due to the associated plasma leakage that may lead to hypovolemic shock. Proper care thus requires identifying patients with DHF among those with suspected dengue so that they can be provided with adequate and prompt fluid replacement. In this study we used seventeen years of pediatric patient data from a prospective cohort study in two hospitals in Thailand to develop models to predict DHF among patients with suspected dengue infection. We produced models for a general hospital setting and for a primary care unit setting lacking lab facilities. The best model using combined data from both hospitals achieved an AUC of 0.90 for the general hospital setting and 0.79 for the primary care unit setting. We then investigated the generalizability of the models by training models with data from one hospital and testing them with data from the other. For some models, we found a significant reduction in performance. Possible sources of this are differences in how attributes are defined or measured and differences in the hematological parameters of the two patient populations. We conclude that while high accuracy prediction of DHF is possible, care must be taken when applying DHF predictive models from one clinical setting to another.
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